A new Reinforcement Learning-based Memetic Particle Swarm Optimizer

Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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“…(2) novel learning schemes [76]; (3) hybrid methods with other algorithm [74]; and (4) local search operator [77].…”

Section: Related Workmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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“…The worst recognition rate was observed with Harmony and MVO. However, PSO and SA have shown good performances due to their ability to exploit the search space more efficiently as compared with other algorithms [16]. Further analysis was conducted by measuring the percentage of overlap between the localized sketch facial components (i.e.…”

Section: Ar Databasementioning

confidence: 99%

“…Particle Swarm Optimization (PSO) was used for face sketch recognition [14,15]. Unfortunately, PSO suffers from fast convergence that result in trapping into local optima [16,17]. To mitigate this challenge, this study adopt an enhanced evolutionary optimizer [18] (i.e.…”

Section: Introductionmentioning

confidence: 99%

Component-based face sketch recognition using an enhanced evolutionary optimizer

2019

Self Cite

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The main aim of this work is to develop a component-based face sketch recognition model. The proposed model adopts an enhanced evolutionary optimizer (EEO) to perform the task of face sketched components localization. EEO is applied to an unknown input sketch to make an automatic localization for its components i.e. eyes, nose, and mouth. After that, HOG features are extracted, and cosine similarity measure is computed to find the best components location. EEO integrates Q-learning algorithm with the simulated annealing (SA) algorithm as a single mode. The Q-learning algorithm is used to control the execution of SA parameters i.e. temperature and the mutation rate at run time. The proposed approach was evaluated on three face sketch recognition benchmark problem which are LFW, AR, and CUHK. The experimental results show that EEO significantly outperform SA as well as other well-known meta-heuristic optimization algorithms such as PSO, Harmony, and MVO.

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“…For example, for the standard PSO, on the one hand, it can quickly fall into local optima at the beginning of the search process; on the other hand, the computational cost will increase with the increase in the sample population size [24]. Therefore, improved PSO algorithms such as the differential evolution particle swarm optimization (DEPSO) [25] and reinforcement-learning-based memetic particle swarm optimization (RLMPSO) [26] came into being. RLMPSO is an improved algorithm from a memetic algorithm (MA) perspective, where the MA is a hybrid algorithm that consist of a local search method, reinforcement learning (RL), and a globally optimal PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

et al. 2021

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Load frequency control (LFC) is necessary to guarantee the safe operation of power systems. Aiming at the frequency and power stability problems caused by load disturbances in interconnected power systems, active disturbance rejection control (ADRC) was designed. There are eight parameters that need to be adjusted for an ADRC, which are challenging to adjust manually, thus limiting the development of this approach in industrial applications. Regardless of the theory or application, there is still no unified and efficient parameter optimization method. The traditional particle swarm optimization (PSO) algorithm suffers from premature convergence and a high computational cost. Therefore, in this paper, we utilize an improved PSO algorithm, a reinforcement-learning-based memetic particle swarm optimization (RLMPSO), for the parameter tuning of ADRC to obtain better control performance for the controlled system. Finally, to highlight the advantages of the proposed RLMPSO-ADRC method and to prove its superiority, the results were compared with other control algorithms in both a traditional non-reheat two-area thermal power system and a non-linear power system with a governor dead band (GDB) and a generation rate constraint (GRC). Moreover, the robustness of the proposed method was tested by simulations with parameter perturbations and different working conditions. The simulation results showed that the proposed method can meet the demand for the frequency deviation to stabilize to 0 in LFC with higher performance, and it is worthy of popularization and application.

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A new Reinforcement Learning-based Memetic Particle Swarm Optimizer

Cited by 114 publications

References 79 publications

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Component-based face sketch recognition using an enhanced evolutionary optimizer

Power System Load Frequency Active Disturbance Rejection Control via Reinforcement Learning-Based Memetic Particle Swarm Optimization

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